As competitors, we consider the TK-SVM classifier with SSTK as in [1]}, as well as LSTM-based models of increasing complexity: (i) a bi-directional LSTM that works on the input sentence (BLSTM); (ii) a bi-directional LSTM that sees the constituency tree as a sequenceNodes are listed as a depth-first enumeration starting from the root node. (Syn-BLSTM); (iii) a combination of (i) and (ii) via a self-attention layer (Dual-Syn-BLSTM). Additionally, we also consider standard tree-aware LSTM models like Tree-LSTM [2]}. At training time, all neural models are regularized by applying early stopping on the validation F1-score.
We hereby report additional evaluation details for each data set:
